Patients with coronary artery disease (CAD) or type-2 diabetes mellitus (DM) sustain large burdens of excess cardiovascular morbidity, mortality, and health care costs. National data reveal that these patients make up about 10% of the adult population and account for the majority of obesity related health care costs. Eighty percent of CAD patients and 90% of DM patients are overweight or obese. Focusing on weight as a risk factor in the secondary prevention of adverse events in these patients, as this study does, is attractive because weight optimization has been shown to improve patient outcomes. This study proposes to use a unique dataset from two large population-based registries of patients with CAD or DM to describe body mass index (BMI) trajectories, patient characteristics associated with those trajectories, and the effect of the trajectories on health care costs. This study addresses the NIH goal of systematic observations of obesity-related behaviors in real world settings. It also addresses the broader long-term goal of designing improved energy balance programs targeted at the most appropriate patients in an effort to reduce future cardiovascular events and other BMI-related complications. Specific aims are, in adult patients with a new diagnosis of CAD or type 2 DM and who meet other study- defined eligibility criteria (n=10,000): 1) to estimate their BMI trajectories 1, 3, and 5 years post-diagnosis; 2) to determine which patient demographic and disease-related characteristics are significantly associated with their 1-, 3-, and 5-year post-diagnosis BMI trajectories; and 3) to examine the association of the 1-, 3-, and 5-year post-diagnosis BMI trajectories with total health care costs. All analyses will be conducted separately for those with newly diagnosed coronary artery disease and those with newly diagnosed type 2 diabetes mellitus. This study will use electronic data from Kaiser Permanente Northwest disease registries, electronic medical record, and other databases. The use of existing data from linked databases will allow conducting a low-cost evaluation of BMI trajectories using growth curve analysis techniques. This evaluation will provide community-based background information useful for determining how to most effectively apply weight management programs. Relevance: This study focuses on patients at highest risk for future health problems related to obesity or overweight-those with coronary artery disease and type 2 diabetes mellitus. Understanding which patients will need the most support to manage weight will allow us to design the most cost-effective future programs.